Unbalanced discounted reward in reinforcement learning : is it a problem?

Question

Discounted rewards seems unbalanced to me.

If we take as example an episode with 4 actions, where each action receive a reward of +1 :

+1 -> +1 -> +1 -> +1

The discounted reward for the last action is : 1
The discounted reward for the first action (considering gamma = 1 for simplicity) is : 4

Intuitively both action are as good as the other, because both received same reward.
But their total reward is different, unbalanced.

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So when we will backpropagate, first action will be favored over last action ?